積體電路製造廠機台數量之配置為積體電路廠中常見的問題，製造積體電路所使用之機台價格昂貴、數量有限且難以完全掌握其製程穩定度。同時，工廠未滿載時常關閉部分機台以節省昂貴的維運成本，但由於積體電路製造過程之複雜多變，工程師們時常難以決定那些機台可以在不影響現有生產計劃的情況之下暫時關機，以節省成本。
本研究以Visual studio 2012 C++發展一套模擬系統並搭配基因演算法用以處理機台數量配置問題之最佳化，允許使用者輸入投片計畫、生產流程預測積體電路廠未來之可能性，並且能以各機群之使用率為目標函數，求解各機群機台數量比例之最佳配置。
本研究將機台數量之配置模擬分為三個部分，分別模擬在特別設計過的投片策略下、三種不同生產流程之下，最佳數量機台比例配置。第一部分為無不確定性事件、無Super hot lot之模擬，為利用模擬系統所建立之理想狀況；第二部分為有不確定性事件、無Super hot lot之模擬，是積體電路廠中較為簡單的狀況，生產流程種類單一且無Super hot lot造成排程上的衝擊；第三部分則是有不確定性事件、有Super hot lot之模擬。一般積體電路廠中，少量多樣的產品，常常是造成排程困難的一大因素，而此部分所模擬使用單一生產流程，將簡化問題的複雜程度。
模擬測試結果顯示本研究所建立之模擬系統可以在不同的生產流程、機型、投片策略下求解出各機群之最佳機台比例配置。並指出各機群之機台比例配置與投片策略以及生產流程之相關性。

The semiconductor manufacturing factory is a very complicated place. In this thesis we develop a simulation- and Genetic Algorithm-based optimization method to systematically solve for an ideal number of workstations deployed for each tool groups. A three-layered discrete event simulation system represents a semiconductor factory, and a Genetic Algorithm-based scheme optimizes the number of active workstations for each tool group. Several scenarios are studied, and it is found that workstation failures result in significant additional number of workstations needed, and that super-hot lots will not cause major impacts as long as their population is low. An early-stage study also indicates that the effect of workstation matching on production efficiency might be greater than that of super-hot lots.

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